
import pandas as pd
from itertools import groupby
from __future__ import division
import operator
import matplotlib.pyplot as plt
import matplotlib.cm
%matplotlib inline
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.colors import Normalize
import numpy as np
import seaborn as sns
import math


data=pd.read_csv(r"C:\Users\HP\Downloads\India Census 2011.csv")
data.fillna(0, inplace=True)
data.head()


# shape
data.shape

(640, 25)


plt.rcParams["figure.figsize"] = 30,24
data.hist()

array([[<AxesSubplot:title={'center':'District_code'}>,
        <AxesSubplot:title={'center':'Population'}>,
        <AxesSubplot:title={'center':'Male'}>,
        <AxesSubplot:title={'center':'Female'}>,
        <AxesSubplot:title={'center':'Literate'}>],
       [<AxesSubplot:title={'center':'Workers'}>,
        <AxesSubplot:title={'center':'Male_Workers'}>,
        <AxesSubplot:title={'center':'Female_Workers'}>,
        <AxesSubplot:title={'center':'Cultivator_Workers'}>,
        <AxesSubplot:title={'center':'Agricultural_Workers'}>],
       [<AxesSubplot:title={'center':'Household_with_phone'}>,
        <AxesSubplot:title={'center':'Hindus'}>,
        <AxesSubplot:title={'center':'Muslims'}>,
        <AxesSubplot:title={'center':'Christians'}>,
        <AxesSubplot:title={'center':'Sikhs'}>],
       [<AxesSubplot:title={'center':'Buddhists'}>,
        <AxesSubplot:title={'center':'Jains'}>,
        <AxesSubplot:title={'center':'Secondary_Education'}>,
        <AxesSubplot:title={'center':'Higher_Education'}>,
        <AxesSubplot:title={'center':'Graduate_Education'}>],
       [<AxesSubplot:title={'center':'Age_Group_0_29'}>,
        <AxesSubplot:title={'center':'Age_Group_30_49'}>,
        <AxesSubplot:title={'center':'Age_Group_50'}>, <AxesSubplot:>,
        <AxesSubplot:>]], dtype=object)


data.style.hide_index()


print(data.groupby('State_name').size())

State_name
ANDAMAN AND NICOBAR ISLANDS     3
ANDHRA PRADESH                 23
ARUNACHAL PRADESH              16
ASSAM                          27
BIHAR                          38
CHANDIGARH                      1
CHHATTISGARH                   18
DADRA AND NAGAR HAVELI          1
DAMAN AND DIU                   2
GOA                             2
GUJARAT                        26
HARYANA                        21
HIMACHAL PRADESH               12
JAMMU AND KASHMIR              22
JHARKHAND                      24
KARNATAKA                      30
KERALA                         14
LAKSHADWEEP                     1
MADHYA PRADESH                 50
MAHARASHTRA                    35
MANIPUR                         9
MEGHALAYA                       7
MIZORAM                         8
NAGALAND                       11
NCT OF DELHI                    9
ORISSA                         30
PONDICHERRY                     4
PUNJAB                         20
RAJASTHAN                      33
SIKKIM                          4
TAMIL NADU                     32
TRIPURA                         4
UTTAR PRADESH                  71
UTTARAKHAND                    13
WEST BENGAL                    19
dtype: int64


'''
Calculating state wise LIT_Education rates
'''
# Group the data state-wise
states_group = data.groupby(by = "State_name")

# List for storing tuples which contain state-name and its corresponding literacy rate
literacy_rate = []

# Iterate through each group (a group has rows with the same state-name throughout the group)
# Here, key contains 'State name' and group contains all the corresponding rows
for key , group in states_group:
    # Total population and total literate population across the state
    total_state_pop = 0
    total_literate_pop = 0
    # Iterate through all the rows of the group to get total state population and total literate state population
    for row in group.iterrows():
        total_state_pop += row[1][3] # 3 is the index of column 'Population'
        total_literate_pop += row[1][6] # 6 is the index of column 'Literate'
    
    # Calculate literacy rate for the state
    rate = (total_literate_pop/total_state_pop)*100
    # Store the result as a tuple in the list literacy_rate, contaning pairs of state names and literacy rates
    literacy_rate.append((key,rate))
    
print ("Statewise literacy rates : \n")
print (literacy_rate)

Statewise literacy rates : 

[('ANDAMAN AND NICOBAR ISLANDS', 77.32414387475991), ('ANDHRA PRADESH', 59.77334542575791), ('ARUNACHAL PRADESH', 55.35810170647822), ('ASSAM', 61.45689155040753), ('BIHAR', 50.43691584466746), ('CHANDIGARH', 76.31228385996495), ('CHHATTISGARH', 60.20670499402666), ('DADRA AND NAGAR HAVELI', 64.94738281511395), ('DAMAN AND DIU', 77.45460375667531), ('GOA', 79.9075105670377), ('GUJARAT', 67.99068069374013), ('HARYANA', 65.47546646422207), ('HIMACHAL PRADESH', 73.41628837330991), ('JAMMU AND KASHMIR', 56.351669069128555), ('JHARKHAND', 55.55958090869887), ('KARNATAKA', 66.5310162908284), ('KERALA', 84.2237101824127), ('LAKSHADWEEP', 81.51164053169543), ('MADHYA PRADESH', 59.00186114469107), ('MAHARASHTRA', 72.5737700262924), ('MANIPUR', 66.8282095977511), ('MEGHALAYA', 60.16419893025995), ('MIZORAM', 77.30316822912015), ('NAGALAND', 67.85103072930934), ('NCT OF DELHI', 75.87450420513153), ('ORISSA', 63.71195527692738), ('PONDICHERRY', 76.71034085418282), ('PUNJAB', 67.42929419668246), ('RAJASTHAN', 55.8368413272501), ('SIKKIM', 72.8740191654779), ('TAMIL NADU', 71.84981419193555), ('TRIPURA', 76.34312370148808), ('UTTAR PRADESH', 57.252497231890196), ('UTTARAKHAND', 68.22083873835896), ('WEST BENGAL', 67.41991702867722)]


#PLOTTING A GEOGRAPHIC MAP FOR STATEWISE Literacy_rate
'''Creating a Map'''
# Create figure 
fig, ax = plt.subplots()
# Create a map with the coordinates determined by the Bounding Box tool
m = Basemap(projection='merc',lat_0=54.5, lon_0=-4.36,llcrnrlon=68.1, llcrnrlat= 6.5, urcrnrlon=97.4, urcrnrlat=35.5)
# Draw map boundary and set the color
m.drawmapboundary(fill_color='#46bcec')
# Fill continents and lakes
m.fillcontinents(color='#f2f2f2',lake_color='#46bcec')
# Draw coast lines
m.drawcoastlines()
'''
STEP 3 : USING SHAPEFILES FOR DRAWING STATES 
'''
# Load the shape file of India
m.readshapefile(r"C:\Users\HP\Downloads\India_SHP\INDIA","INDIA")

'''
STEP 4 : CREATING A DATAFRAME MAPPING SHAPES TO STATE NAME AND LITERACY RATES
'''
# Create an empty list to hold graduate rates
lit_rate = []
# Iterate through all rows of INDIA_info file
# Each row is a dictionary. Here, we try to map state name with literacy rate
for state_info in m.INDIA_info:
    # Get the state in uppercase, as our csv file has state names in upper case
    state = state_info['ST_NAME'].upper()
    # initialize rate = 0
    rate = 0
    
    # In literacy_rate (containing tuples of state name and graduate_education rates), search for state 'state'
    # Append its corresponding graduate_education rate to gra_rate
    for x in literacy_rate:
        if x[0] == state:
            rate = x[1]
            break
    lit_rate.append(rate) 
    

    

# Create a dataframe containing shapes, state names and graduate_education rates    
df_poly = pd.DataFrame({
    'shapes': [Polygon(np.array(shape), True) for shape in m.INDIA],
    'area': [area['ST_NAME'] for area in m.INDIA_info],
    'lit_rate' : lit_rate
    })

'''
STEP 5 : USING DATA TO COLOR AREAS
'''
# Get all the shapes
shapes = [Polygon(np.array(shape), True) for shape in m.INDIA]
# Create a colormap
cmap = plt.get_cmap('Blues')   
# Create a patch collection. Create patches on the top of the map, not beneath it (zorder=2)
pc = PatchCollection(shapes, zorder=2)

norm = Normalize()
# Set color according to the literacy rate of the state
pc.set_facecolor(cmap(norm(df_poly['lit_rate'].fillna(0).values)))
ax.add_collection(pc)

# Create a mapper to map color intensities to values
mapper = matplotlib.cm.ScalarMappable(cmap=cmap)
mapper.set_array(lit_rate)
plt.colorbar(mapper, shrink=0.4)
# Set title for the plot
ax.set_title("Literacy_rate")
# Change plot size and font size
plt.rcParams['figure.figsize'] = (15,15)
plt.rcParams.update({'font.size': 20})
plt.show()


df_MAHARASHTRA= data.loc[data['State_name'] == 'MAHARASHTRA']


print("rows = %s"%(str(len(df_MAHARASHTRA))))
df_MAHARASHTRA.head()

rows = 35


df_GUJARAT = data.loc[data['State_name'] == 'GUJARAT']
df_GUJARAT.head()


def similar_districts(df1, df2):
    # Set indices for both the data frames
    df1.set_index('District_code')
    df2.set_index('District_code')
    
    # The similarity matrix of size len(df1) X len(df2)
    main_diff = []
    
    # Iterate through rows of df1
    for row1 in df1.iterrows(): 
        # Create list to hold similarity score of row1 with other rows of df2
        diff=[]
        # Iterate through rows of df2
        for row2 in df2.iterrows():
            # Calculate sum of squared differences
            dist = 0
            for column in list(data)[3:]:
                max_col = max(data[column])
                min_col = min(data[column]) 
    
                dist += pow((row1[1][column] - row2[1][column])/(max_col - min_col),2)
            # Take sqrt and inverse the result
            diff.append(1/math.sqrt(dist))
        # Append similarity scores
        main_diff.append(diff)

    # Find the max value of similarity score from lists of lists
    max_val = 0
    max_index1 = 0
    max_index2 = 0
    for i in range(len(main_diff)):

        for j in range(len(main_diff[i])):
            if(main_diff[i][j] > max_val):
                max_val = main_diff[i][j]
                max_index1 = i
                max_index2 = j

    print ("%s from GUJARAT and %s from  MAHARASHTRA are most similar" %(df1['District_name'].iloc[max_index1],
                                                                    df2['District_name'].iloc[max_index2]))
    return main_diff
    
sim_matrix = similar_districts(df_GUJARAT, df_MAHARASHTRA)

Kheda from GUJARAT and Dhule from  MAHARASHTRA are most similar


norm=Normalize()
ax = plt.axes()
# Plot the matrix using a heatmap from seaborn
# Values of the matrix are normalized so as to fall between 0 and 1
sns.heatmap(norm(sim_matrix), xticklabels=df_MAHARASHTRA['District_name'],yticklabels=df_GUJARAT['District_name'],
            linewidths=0.05,cmap='Blues').set_title("SIMILARITY MATRIX FOR DISTRICTS OF GUJARAT AND MAHARASHTRA")
plt.rcParams['figure.figsize'] = (24,24)


# Create a group w.r.t. State name
states_group = data.groupby(by = "State_name")

# Lists for Female population and Female_Workers values for each state
Female = []
Female_Workers = []

# Iterate through the groups
for key , group in states_group:
    total_female = 0
    total_female_workers = 0
    
    # Iterate through each group and calculate total agri workers and total households with mobiles
    for row in group.iterrows():
    
        #total_mobi_pop += row[1][58] # 58 = Index of column 'Households_with_Telephone_Mobile_Phone_Landline_only'
        total_female += row[1][5] # 59 = Index of column 'Households_with_Telephone_Mobile_Phone_Mobile_only'
        total_female_workers += row[1][9] # 22 = Index of 'Agricultural Workers'
    
    # Append both values to respective lists
    Female.append((key,total_female))
    Female_Workers.append((key,total_female_workers))
    
# Create a dataframe holding state name, households_with_mobile and agri_workers 
df_mobile_penetration =  pd.DataFrame({
        'state' : [x[0] for x in states_group] ,
        'Female': [x[1] for x in Female],
        'Female_Workers' : [x[1] for x in Female_Workers]
        
    })

# View the dataframe
df_mobile_penetration


#PLOTTING STATEWISE DISTRIBUTION OF Female_Workers AND Female
from numpy import *
ind = arange(35)
width = 0.4

fig, ax = plt.subplots()
plt.xlim(0,22000000)
rects1 = ax.barh(ind, df_mobile_penetration['Female_Workers'],width,color='b',align='center')
rects2 = ax.barh(ind+width, df_mobile_penetration['Female'],width,color='lightblue',align='center')
ax.set_xlabel('Population')
ax.set_title('Female population IN VARIOUS STATES W.R.T. Female Workers')
ax.set_yticks(ind + width / 2)
ax.set_yticklabels((x for x in df_mobile_penetration['state']))
ax.legend((rects1[0], rects2[0]), ('Female Workers', 'Population of Female in state'))
plt.rcParams.update({'font.size': 25})
plt.rcParams['figure.figsize'] = (25,25)
plt.show()


#DISTRIBUTION OF HOUSEHOLDS USING MOBILES AND AGRICULTURAL WORKERS
plt.figure(figsize=(10,6))
pop = pd.DataFrame(data.groupby(['District_name'])['Population', 'Agricultural_Workers', 'Household_with_phone'].sum()).sort_values(by=['Population'], ascending=False)
sns.scatterplot(data=pop, x='Household_with_phone', y='Agricultural_Workers', hue='Population',size='Population', legend='brief', sizes=(2, 200))
plt.show()

<ipython-input-292-ae59505532ce>:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  pop = pd.DataFrame(data.groupby(['District_name'])['Population', 'Agricultural_Workers', 'Household_with_phone'].sum()).sort_values(by=['Population'], ascending=False)


data.describe()


# difference between male and female workers for each state
(data.Male_Workers - data.Female_Workers).describe()

count    6.400000e+02
mean     2.843608e+05
std      3.065455e+05
min     -1.329100e+04
25%      9.055525e+04
50%      2.047975e+05
75%      3.718278e+05
max      2.318754e+06
dtype: float64


#bar chart of Workers comparison between different states
plot_barh(data, 'State_name', 'Workers', title = 'Workers comparison \
between different states' , xlabel = 'Workers')


#bar chart of female_Workers comparison between different states
plot_barh(data, 'State_name', 'Female_Workers', title = 'Female workers comparison \
between different states' , xlabel = 'Female_Workers')


#bar chart of Male_Workers comparison between different states
plot_barh(data, 'State_name', 'Male_Workers', title = 'Male workers comparison \
between different states' , xlabel = 'Male_Workers')


#bar chart of Agricultural_Workers comparison between different states
plot_barh(data, 'State_name', 'Agricultural_Workers', title = 'Agricultural_Workers comparison \
between different states' , xlabel = 'Agricultural_Workers')


#bar chart of Cultivator_Workers comparison between different states
plot_barh(data, 'State_name', 'Cultivator_Workers', title = 'Cultivator_Workers comparison \
between different states' , xlabel = 'Cultivator_Workers')


sns.pairplot(data)

<seaborn.axisgrid.PairGrid at 0x2163883a640>


#pie chart of Percentage population of india by religion 2011
t=data['Population'].sum()
def value(x):
    a=(x/t)*100
    return a
    
name=['Hindu', 'Muslims', 'Christians', 'Sikhs','Buddhists','Jains']
h=data['Hindus'].sum()
m=data['Muslims'].sum()
c=data['Christians'].sum()
s=data['Sikhs'].sum()
b=data['Buddhists'].sum()
j=data['Jains'].sum()

people = [value(h),value(m),value(c),value(s),value(b),value(j)]
print("Percentage population of india by religion 2011")
plt.pie(people, labels=name,autopct='%0.f%%', shadow=True,startangle=180)

Percentage population of india by religion 2011

([<matplotlib.patches.Wedge at 0x2163a15e190>,
  <matplotlib.patches.Wedge at 0x2163a15ea60>,
  <matplotlib.patches.Wedge at 0x2163a154370>,
  <matplotlib.patches.Wedge at 0x2163a154c70>,
  <matplotlib.patches.Wedge at 0x2164d090640>,
  <matplotlib.patches.Wedge at 0x2164d090eb0>],
 [Text(0.9003242568507431, -0.6319938548167675, 'Hindu'),
  Text(-0.7872736625823674, 0.7682448699497737, 'Muslims'),
  Text(-1.0659615519790893, 0.27152526531122606, 'Christians'),
  Text(-1.0918169877687385, 0.1339241024595583, 'Sikhs'),
  Text(-1.0988660298929538, 0.04993444049249349, 'Buddhists'),
  Text(-1.0999253068002786, 0.01281871523646395, 'Jains')],
 [Text(0.49108595828222346, -0.3447239208091459, '81%'),
  Text(-0.4294219977722004, 0.41904265633624016, '14%'),
  Text(-0.5814335738067758, 0.14810469016975963, '2%'),
  Text(-0.5955365387829482, 0.07304951043248634, '2%'),
  Text(-0.5993814708507019, 0.02723696754136008, '1%'),
  Text(-0.5999592582546973, 0.006992026492616699, '0%')])

	District_code	State_name	District_name	Population	Male	Female	Literate	Workers	Male_Workers	Female_Workers	...	Christians	Sikhs	Buddhists	Jains	Secondary_Education	Higher_Education	Graduate_Education	Age_Group_0_29	Age_Group_30_49	Age_Group_50
0	1	JAMMU AND KASHMIR	Kupwara	870354	474190	396164	439654	229064	190899	38165	...	1700	5600	66	39	74948	39709	21751	600759	178435	89679
1	2	JAMMU AND KASHMIR	Badgam	753745	398041	355704	335649	214866	162578	52288	...	1489	5559	47	6	66459	41367	27950	503223	160933	88978
2	3	JAMMU AND KASHMIR	Leh(Ladakh)	133487	78971	54516	93770	75079	53265	21814	...	658	1092	88635	103	16265	8923	6197	70703	41515	21019
3	4	JAMMU AND KASHMIR	Kargil	140802	77785	63017	86236	51873	39839	12034	...	604	1171	20126	28	16938	9826	3077	87532	35561	17488
4	5	JAMMU AND KASHMIR	Punch	476835	251899	224936	261724	161393	117677	43716	...	958	11188	83	10	46062	29517	13962	304979	109818	61334

	District_code	State_name	District_name	Population	Male	Female	Literate	Workers	Male_Workers	Female_Workers	...	Christians	Sikhs	Buddhists	Jains	Secondary_Education	Higher_Education	Graduate_Education	Age_Group_0_29	Age_Group_30_49	Age_Group_50
496	497	MAHARASHTRA	Nandurbar	1648295	833170	815125	906509	792065	442898	349167	...	8467	685	4969	6191	160961	113019	58461	1011352	390419	241534
497	498	MAHARASHTRA	Dhule	2050862	1054031	996831	1293916	936370	570448	365922	...	3653	1422	13405	12818	290656	182739	114931	1167569	513908	350747
498	499	MAHARASHTRA	Jalgaon	4229917	2197365	2032552	2891882	1863571	1186472	677099	...	7091	3091	143865	27404	627198	375643	229987	2352671	1080237	763529
499	500	MAHARASHTRA	Buldana	2586258	1337560	1248698	1879874	1219641	734024	485617	...	3531	1668	364229	12242	406379	228266	113783	1467457	641840	473071
500	501	MAHARASHTRA	Akola	1813906	932334	881572	1411281	768154	520362	247792	...	4483	1504	328033	10205	291391	205723	112574	998769	483200	327932

	District_code	State_name	District_name	Population	Male	Female	Literate	Workers	Male_Workers	Female_Workers	...	Christians	Sikhs	Buddhists	Jains	Secondary_Education	Higher_Education	Graduate_Education	Age_Group_0_29	Age_Group_30_49	Age_Group_50
467	468	GUJARAT	Kachchh	2092371	1096737	995634	1252319	776228	624704	151524	...	6192	6353	490	25312	157284	61366	60386	1278604	524052	286086
468	469	GUJARAT	Banas Kantha	3120506	1610379	1510127	1704923	1248600	836268	412332	...	1686	486	281	12659	226765	105372	66950	1984701	738412	394793
469	470	GUJARAT	Patan	1343734	694397	649337	837913	554830	383074	171756	...	916	337	66	3602	129584	57494	46355	786357	341381	213465
470	471	GUJARAT	Mahesana	2035064	1056520	978544	1502645	812064	588699	223365	...	1954	1082	146	7087	266108	139208	130723	1119451	554432	357578
471	472	GUJARAT	Sabar Kantha	2428589	1244231	1184358	1578734	1084878	681435	403443	...	6375	614	168	11110	333650	198963	110817	1393131	630963	400905

	state	Female	Female_Workers
0	ANDAMAN AND NICOBAR ISLANDS	177710	31646
1	ANDHRA PRADESH	42138631	15237311
2	ARUNACHAL PRADESH	669815	237384
3	ASSAM	15266133	3428130
4	BIHAR	49821295	9502798
5	CHANDIGARH	474787	75977
6	CHHATTISGARH	12712303	5046359
7	DADRA AND NAGAR HAVELI	149949	37868
8	DAMAN AND DIU	92946	13837
9	GOA	719405	157712
10	GUJARAT	28948432	6766833
11	HARYANA	11856728	2109872
12	HIMACHAL PRADESH	3382729	1516049
13	JAMMU AND KASHMIR	5900640	1127623
14	JHARKHAND	16057819	4673505
15	KARNATAKA	30128640	9602481
16	KERALA	17378649	3167494
17	LAKSHADWEEP	31350	3435
18	MADHYA PRADESH	35014503	11427163
19	MAHARASHTRA	54131277	16811003
20	MANIPUR	1417208	565202
21	MEGHALAYA	1475057	481910
22	MIZORAM	541867	195965
23	NAGALAND	953853	426765
24	NCT OF DELHI	7800615	825023
25	ORISSA	20762082	5638934
26	PONDICHERRY	635442	112037
27	PUNJAB	13103873	1823205
28	RAJASTHAN	32997440	11589179
29	SIKKIM	287507	113780
30	TAMIL NADU	36009055	11449703
31	TRIPURA	1799541	424195
32	UTTAR PRADESH	95331831	15967953
33	UTTARAKHAND	4948519	1320354
34	WEST BENGAL	44467088	8040308

	District_code	Population	Male	Female	Literate	Workers	Male_Workers	Female_Workers	Cultivator_Workers	Agricultural_Workers	...	Christians	Sikhs	Buddhists	Jains	Secondary_Education	Higher_Education	Graduate_Education	Age_Group_0_29	Age_Group_30_49	Age_Group_50
count	640.000000	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	...	6.400000e+02	6.400000e+02	640.000000	640.000000	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02	6.400000e+02
mean	320.500000	1.891961e+06	9.738598e+05	9.181011e+05	1.193186e+06	7.529514e+05	5.186561e+05	2.342953e+05	1.856387e+05	2.255513e+05	...	4.346811e+04	3.255174e+04	13192.143750	6955.864062	2.051046e+05	1.241736e+05	1.090132e+05	1.102826e+06	4.820189e+05	3.001005e+05
std	184.896367	1.544380e+06	8.007785e+05	7.449864e+05	1.068583e+06	6.071622e+05	4.412388e+05	1.913541e+05	1.514895e+05	2.216222e+05	...	1.087273e+05	1.603859e+05	56347.946567	23624.010655	2.164778e+05	1.304154e+05	1.502260e+05	8.887434e+05	4.183230e+05	2.549556e+05
min	1.000000	8.004000e+03	4.414000e+03	3.590000e+03	4.436000e+03	3.555000e+03	2.228000e+03	1.327000e+03	0.000000e+00	0.000000e+00	...	1.710000e+02	3.000000e+00	1.000000	0.000000	8.090000e+02	6.560000e+02	3.560000e+02	5.113000e+03	2.088000e+03	7.860000e+02
25%	160.750000	8.178610e+05	4.171682e+05	4.017458e+05	4.825982e+05	3.284688e+05	2.294168e+05	9.420350e+04	7.491025e+04	5.284175e+04	...	1.951750e+03	2.537500e+02	163.750000	124.750000	7.078575e+04	4.234700e+04	2.799200e+04	4.786540e+05	2.101965e+05	1.193285e+05
50%	320.500000	1.557367e+06	7.986815e+05	7.589200e+05	9.573465e+05	6.223410e+05	4.288515e+05	1.929265e+05	1.501250e+05	1.645300e+05	...	6.079500e+03	7.375000e+02	372.000000	741.000000	1.523325e+05	8.998700e+04	6.624350e+04	8.896820e+05	3.905085e+05	2.421020e+05
75%	480.250000	2.583551e+06	1.338604e+06	1.264277e+06	1.602260e+06	1.036544e+06	6.991510e+05	3.163658e+05	2.553015e+05	3.258982e+05	...	2.750850e+04	2.800000e+03	1942.500000	4150.750000	2.629160e+05	1.583555e+05	1.299488e+05	1.491204e+06	6.525142e+05	4.045875e+05
max	640.000000	1.106015e+07	5.865078e+06	5.195070e+06	8.227161e+06	4.492767e+06	3.363123e+06	1.239177e+06	1.013050e+06	1.227956e+06	...	1.248137e+06	1.863408e+06	668050.000000	343639.000000	2.018079e+06	1.196965e+06	1.733274e+06	6.184519e+06	3.241888e+06	1.921143e+06

District_code	State_name	District_name	Population	Male	Female	Literate	Workers	Male_Workers	Female_Workers	Cultivator_Workers	Agricultural_Workers	Household_with_phone	Hindus	Muslims	Christians	Sikhs	Buddhists	Jains	Secondary_Education	Higher_Education	Graduate_Education	Age_Group_0_29	Age_Group_30_49	Age_Group_50
1	JAMMU AND KASHMIR	Kupwara	870354	474190	396164	439654	229064	190899	38165	34680	56759	7946	37128	823286	1700	5600	66	39	74948	39709	21751	600759	178435	89679
2	JAMMU AND KASHMIR	Badgam	753745	398041	355704	335649	214866	162578	52288	55299	36630	29102	10110	736054	1489	5559	47	6	66459	41367	27950	503223	160933	88978
3	JAMMU AND KASHMIR	Leh(Ladakh)	133487	78971	54516	93770	75079	53265	21814	20869	1645	1020	22882	19057	658	1092	88635	103	16265	8923	6197	70703	41515	21019
4	JAMMU AND KASHMIR	Kargil	140802	77785	63017	86236	51873	39839	12034	8266	3763	1222	10341	108239	604	1171	20126	28	16938	9826	3077	87532	35561	17488
5	JAMMU AND KASHMIR	Punch	476835	251899	224936	261724	161393	117677	43716	54264	31583	3930	32604	431279	958	11188	83	10	46062	29517	13962	304979	109818	61334
6	JAMMU AND KASHMIR	Rajouri	642415	345351	297064	364109	290912	184752	106160	136527	24016	4656	221880	402879	983	15513	189	26	65921	35804	18576	404903	153165	83319
7	JAMMU AND KASHMIR	Kathua	616435	326109	290326	389204	200431	161548	38883	69533	21566	3952	540063	64234	1828	9551	24	16	91522	47694	24330	357864	160123	97684
8	JAMMU AND KASHMIR	Baramula	1008039	534733	473306	545149	304200	249581	54619	57495	62246	15084	30621	959185	1497	14770	140	29	107837	57932	48285	636524	239659	130513
9	JAMMU AND KASHMIR	Bandipore	392232	207680	184552	185979	149317	101380	47937	28232	32882	20484	8439	382006	572	555	44	17	35630	18644	12721	252378	90465	48802
10	JAMMU AND KASHMIR	Srinagar	1236829	651124	585705	748584	407188	333151	74037	12228	10408	20095	42540	1177342	2746	12187	285	74	176409	132727	121856	693238	351561	190330
11	JAMMU AND KASHMIR	Ganderbal	297446	158720	138726	143276	100890	73305	27585	15337	19312	7630	5592	290581	406	486	25	8	28247	15005	9633	194165	66945	35911
12	JAMMU AND KASHMIR	Pulwama	560440	293064	267376	293958	188420	138428	49992	49746	34080	10615	13840	535159	1109	9440	35	6	67368	40931	31286	342601	137032	80111
13	JAMMU AND KASHMIR	Shupiyan	266215	136480	129735	136500	87721	63931	23790	47424	10835	3395	3116	262263	429	178	6	1	28617	16501	10782	170086	60752	35158
14	JAMMU AND KASHMIR	Anantnag	1078692	559767	518925	545532	389684	244032	145652	140957	76364	16322	13180	1057005	1449	6140	55	7	97121	52407	41570	704145	239988	133519
15	JAMMU AND KASHMIR	Kulgam	424483	217620	206863	209085	159990	102317	57673	69173	29928	6193	4267	418076	460	1035	27	25	45383	24214	18240	266858	99870	57253
16	JAMMU AND KASHMIR	Doda	409936	213641	196295	219083	151912	101144	50768	69947	12199	1932	187621	220614	472	422	21	22	48359	31416	12647	263576	93189	52744
17	JAMMU AND KASHMIR	Ramban	283713	149132	134581	124065	87212	68476	18736	41394	8810	2538	81026	200516	414	753	43	3	22591	13331	4923	188526	61604	33220
18	JAMMU AND KASHMIR	Kishtwar	230696	120165	110531	107506	82974	56779	26195	39271	16296	1186	93931	133225	277	450	2094	3	19550	14521	5438	145931	53415	31084
19	JAMMU AND KASHMIR	Udhampur	554985	296784	258201	322354	243672	163845	79827	113020	11396	2641	489044	59771	1962	3418	106	57	55024	29657	17345	339145	140785	74757
20	JAMMU AND KASHMIR	Reasi	314667	166461	148206	150542	144767	88755	56012	96147	4009	534	153898	156275	1208	3107	15	7	24066	14064	4733	203967	70767	39743
21	JAMMU AND KASHMIR	Jammu	1529958	813821	716137	1137135	508622	417456	91166	68613	36873	10589	1289240	107489	12104	114272	470	1987	261852	159257	146501	816827	435895	275585
22	JAMMU AND KASHMIR	Samba	318898	169124	149774	228139	92526	81952	10574	16894	6105	1520	275311	22950	2306	17961	48	16	61407	30503	14055	177277	88246	53022
23	HIMACHAL PRADESH	Chamba	519080	261320	257760	323842	294035	158791	135244	196665	9193	4521	479782	32455	1559	2745	1740	57	64515	40440	15506	306555	130606	81566
24	HIMACHAL PRADESH	Kangra	1510075	750591	759484	1152640	675170	403756	271414	303007	54849	15662	1461140	19797	3023	8929	14511	194	284124	192936	91236	766581	419172	319420
25	HIMACHAL PRADESH	Lahul AND Spiti	31564	16588	14976	21845	19295	10763	8532	11227	572	167	11649	74	212	35	19572	3	3934	3557	2378	15928	9181	6200
26	HIMACHAL PRADESH	Kullu	437903	225452	212451	307672	269084	148829	120255	197141	12372	2707	415669	2974	1568	1396	15377	94	58122	39081	23168	244727	120510	72282
27	HIMACHAL PRADESH	Mandi	999777	498065	501712	723747	572671	297464	275207	387944	15822	7134	981412	9460	876	4081	2628	43	180188	128549	54139	524285	279321	194661
28	HIMACHAL PRADESH	Hamirpur	454768	217070	237698	358091	241931	118724	123207	147103	8824	3347	449412	3711	243	591	95	293	91184	64363	35620	224044	123941	106641
29	HIMACHAL PRADESH	Una	521173	263692	257481	399715	215346	141612	73734	84800	19604	3427	480138	14275	390	26009	56	70	105204	60626	32120	273223	142351	105358
30	HIMACHAL PRADESH	Bilaspur	381956	192764	189192	287620	205871	111543	94328	127169	4138	2561	371973	6984	236	2387	113	10	64730	45695	25474	193671	107367	80497
31	HIMACHAL PRADESH	Solan	580320	308754	271566	428578	298737	190001	108736	141267	10283	4969	548579	14678	1452	13926	574	591	95232	59363	36803	321533	167168	90808
32	HIMACHAL PRADESH	Sirmaur	529855	276289	253566	362645	280083	169399	110684	182958	10730	5313	476830	33215	577	15501	2645	236	67080	38282	23352	310244	136839	82471
33	HIMACHAL PRADESH	Shimla	814010	425039	388971	612642	430926	258628	172298	249786	26114	7967	791449	11810	2025	4047	3262	185	141490	95341	80414	425721	241956	145577
34	HIMACHAL PRADESH	Kinnaur	84121	46249	37872	60699	56273	33863	22410	32995	2537	944	64732	448	485	249	18086	29	11140	7871	5118	42360	26599	15075
35	PUNJAB	Gurdaspur	2298323	1212617	1085706	1634719	757138	637652	119486	147912	100684	30903	1074332	27667	176587	1002874	405	580	372317	243539	104345	1261421	611353	423301
36	PUNJAB	Kapurthala	815168	426311	388857	576567	284165	235562	48603	55252	42387	9861	336124	10190	5445	453692	6662	553	128828	77442	48221	433331	222324	157833
37	PUNJAB	Jalandhar	2193590	1145211	1048379	1622537	774472	639484	134988	85695	66664	29762	1394329	30233	26016	718363	11385	4011	339758	216646	162965	1159156	616431	416449
38	PUNJAB	Hoshiarpur	1586625	809057	777568	1199699	499822	412506	87316	89772	74362	15990	1000743	23089	14968	538208	3476	2034	301435	176013	93618	831741	422782	330671
39	PUNJAB	Shahid Bhagat Singh Nagar	612310	313291	299019	438459	204329	170252	34077	42608	22626	9011	401368	6829	1479	192885	5885	695	101970	61643	26893	318597	163305	128310
40	PUNJAB	Fatehgarh Sahib	600163	320795	279368	426033	214783	182777	32006	44245	21389	8048	152851	16808	1698	427521	48	178	95073	59555	34990	321018	166781	111028